Methods and tools for reading culture at scale.
Encode texts, map archives, model networks, and build sustainable digital scholarship — taught by practising humanists.
All Courses
Designed for senior students, this course moves on to deeper technical issues, influential critiques, and the lasting legacies of both thinkers. **Prerequisites:** "Wittgenstein and Carnap: The Core Debates" or at least two prior courses in logic and/or 20th-century philosophy. **Learning outcomes:** - Critically evaluate Wittgenstein's arguments in Remarks on the Foundations of Mathematics. <br>- Analyze the structure of the Private Language Argument. <br>- Assess the impact of Quine's critique of the analytic-synthetic distinction. <br>- Trace the legacies of formalism and practice-based philosophy.
This is a collection of intermediate and advanced modules for mastering Topic in the theoretical and practical study of Artificial Intelligence in computer science, philosophy of computing, cognitive sciences, and philosophy of artificial intelligence.
Learn how a real, published research game was built — from a humanities research question about immigration bureaucracy, through game design and full-stack engineering with AI/RAG, to ethics review and academic publication — by reading the actual codebase and design artifacts.
An advanced philosophy course on the emerging field of conceptual engineering — the practice of evaluating and revising the concepts we use. Drawing on work by Haslanger, Cappelen, Plunkett, Burgess, and Appiah, the course asks: when should we change what a word means, and who has the authority to do so? Topics include social ontology, metalinguistic negotiation, amelioration, and the politics of conceptual revision.
An applied data science course using real climate and disaster datasets. Covers data cleaning, exploratory analysis, visualisation, and introductory modelling — with datasets including global temperature records, disaster event logs, and environmental impact data. Contextualises data science methods within environmental humanities and policy research.
An applied course exploring museum and cultural heritage collections through data science methods. Covers collection data structures, metadata standards (Dublin Core, CIDOC-CRM), exploratory analysis, visualisation, and network mapping of cultural objects — using real museum CSV datasets as primary material.
A project-oriented course on taking Digital Humanities ideas from conception to working product in African contexts. Covers value-sensitive design, technology ethics, trust and system design, AI infrastructure and platform choices, large language models and product fit, prompting and value alignment, data infrastructure and governance, metadata and knowledge representation, and UX/UI for user-centred DH tools.
A hands-on, practical introduction to data science for Digital Humanities — taught through the 2026 Canadian Migration Data Challenge. Covers descriptive statistics, regression, clustering, machine learning, geospatial analysis, and policy reasoning using real Census housing data from Montréal, Toronto, Edmonton, and Vancouver.
A practicum course drawing on Digital Humanities summer school materials — covering the full DH methods toolkit: text encoding (TEI), distant reading, network analysis, data visualisation with Gephi and Tableau, and web scraping with Python. Suitable for humanities researchers entering the DH space.